EVIDENCE BASED MEDICAL QUESTION ANSWERING SYSTEM USING KNOWLEDGE GRAPH PARADIGM

Evidence Based Medicine (EBM) is the process of systematically finding, judging, and using research findings as the basis for clinical decisions and has become the standard of medical practice. There are countless new studies and research being published daily. Keeping track of each of them is impossible, not to mention needing to read and comprehend them. While search engines can help healthcare professionals search for a topic with suggesting relevant papers on the topic, healthcare professionals still need to go through the papers and extract relevant information themselves. This is a very time-consuming task as one study on Information Retrieval (IR) practices of healthcare information professionals that it takes on average 4 hours for healthcare information professionals to finish a search task. Moreover, a systematic review study on the barriers to medical residents' practicing of evidence-based medicine revealed that two of the most frequently mentioned barriers for residents were limitations in available time, knowledge, and skills. In this project, we address both problems by building a Medical Question Answering (QA) system that employees semi-supervised information extraction methods in Natural Language Processing (NLP) to construct a large scale Knowledge Graph (KG) from the extracted facts from a large repository of medical research publications. Then, the system translates a given user’s question in a natural language to the KG efficiently to extract relevant answers based on evidences to present in a user-friendly manner. The system returns a compilation of summaries for the related evidences with one sentence summary for each evidence relevant to the user’s question and the reference to the full publication. The system can help address the barriers of knowledge and skills by providing comprehensive summary of the evidences for a given question in a natural language that eliminates the need to formulate complex structured queries. The system was evaluated on a real-life test dataset collected from healthcare professionals, and the evaluation results showed performances comparable to the state-of-the-art Biomedical QA systems in the literature.